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Microcomputer-Based Approaches for Preventing Drug and

Alcohol Abuse Among Adolescents f rom Ethnic-Racial MinorityBackgrounds

Michael S. Moncher, Clifford A. Parms, Mario A. Orlandi, Steven P. Schinke, Samuel O.

Miller, Josephine Palleja, and Mary B. Schinke

Columbia University and American Health Foundation

Abstract

This study was designed to empirically assess the potential of microcomputer-based intervention

with black adolescents from economically disadvantaged backgrounds. Subjects were 26, 11 through

14-year-old black females and males recruited from three boroughs in New York City. A sample

task was administered via microcomputer system followed by a postintervention measurementbattery. Observational measures were also employed to assess interactional variables. Subjects

attitudes toward educational content in general, and toward drug and alcohol information delivery

in particular, appeared to be a significant intervening variable that could alter the overall efficacy of

computer-delivered interventions. Both observational and postintervention measures indicated an

overall positive subject response to computer-administered instruction. In contrast, however,

respondents indicated a negative response to microcomputer delivery of drug and alcohol related

materials. Results of the experiment are discussed along with rationales and future research

directions.

INTRODUCTION AND OVERVIEW

Recent years have witnessed a growing use of computer-assisted instruction (CAI) for a varietyof purposes. As noted by Elwork and Gutkin (1985):

The computerization of our society has already begun. We have little doubt that it will proceed

at an ever quickening pace. The central question confronting behavioral scientists is whether

we want it to occur with our input (p. 14).

Studies of CAI programs find consistent increases in adolescents performance, rate of

learning, and motivation (Burns & Bozeman, 1981; Hartley & Levell, 1978; Kulik, Bangert,

& Williams, 1983; McCollister, Burts, Wright, & Hildreth, 1986; Menis, Synder, & Ben-

Kohav, 1980; Ragosta, 1983).

CAI can provide several important instructional advantages in health education. Because it is

interactive, self-directed software is intrinsically motivating. With this software, adolescents

can elicit health information in areas of greatest concern. Techniques such as branching can

further personalize youths learning, permitting much flexibility and considerable involvement

(Anand & Ross, 1987; Bosworth, Gustafson, Hawkins, Chewning, & Day, 1983). With drug

and alcohol issues, self-directed formats give youths access to potentially more objective

information. Additionally, this information can be obtained confidentially, which is essential

Requests for reprints should be addressed to Michael S. Moncher, Columbia University School of Social Work, 622 West 113th Street,New York, NY 10025..

NIH Public AccessAuthor ManuscriptComput Human Behav. Author manuscript; available in PMC 2007 March 26.

Published in final edited form as:

Comput Human Behav. 1989 ; 5(2): 7993.

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to young people exploring current knowledge on illegal and value-laden topics (Kahn, 1987).

In addition to helping youths debunk myths and learn about the substances they commonly

use, the objectivity afforded by self-directed computer formats reduces the likelihood of biased

information that can mar drug and alcohol prevention efforts.

Interacting with self-directed computer programs may not only equip black adolescents with

a repertoire of knowledge and skills around drugs and alcohol, but also may instill in young

people the confidence to apply that learning. Indeed, research points to such benefits for youngpeople in general (Robertson, Ladewig, Strickland, & Boschung 1987) and adolescents from

lower socioeconomic and disadvantaged backgrounds in particular (Daron & Rich, 1981;

Mervarech & Rich, 1985; Saracho, 1982). Last, the interactive computer medium may heighten

black youths self-efficacy by letting them control their learning and by showing them that

they can exert independent decision making about drugs, alcohol, and other personal choice

behaviors.

Recent research on computer-based instruction is encouraging. Watkins (1986) reported that

six months after the delivery of a microcomputer-based instructional program, first-grade

participants had improved math and cognitive reasoning skills relative to students who received

a noncomputerized intervention. In a meta-analysis of 32 comparative studies, Kulik, Kulik,

and Bangert-Drowns (1985) found that computer-assisted and computer-managed instructional

programs effected higher outcome changes than traditional, noncomputerized programs. Forinstance, children who received computer-assisted programs, which accounted for the majority

of studies examined in the meta-analysis, achieved post-intervention gains averaging .47

standard deviations, or from the 50th to the 68th percentile in outcome measurement scores.

Earlier meta-analyses of microcomputer instructional interventions also showed positive

effects. Chambers and Sprecher (1980) reviewed all available research on the topic and learned

that computer-assisted instruction: (a) produced equal or better learning outcomes as compared

to traditional instruction; (b) reduced learning time over traditional instruction; (c) improved

student attitudes toward computers as a learning vehicle; and (d) promoted professionals

acceptance and use of computer applications. Similarly, a meta-analysis of computer-based

learning at the secondary education level by Kulik et al. (1983) found that when juxtaposed

with conventional classroom instruction, computer methods resulted in greater student

achievement and in reduced time for learning a topic.

Despite the potential of self-directed health education, such computer-based programs are slow

in coming. While presenting strong support for computer-mediated instruction in general, most

research to date has not focused primarily on behaviorally oriented interventions. One such

intervention, tested by Tombari, Fitzpatrick, and Childress (1985) showed significant posttest

gains. In a controlled experimental design involving fifth-graders, Tombari et al. tested an

intervention involving such procedures as goal setting, goal rehearsal, feedback, contingent

reinforcement, schedule attenuation, and maintenance of behavior change. Each procedure was

delivered and monitored by personal computer. A reversal design indicated that the

intervention reduced disruptive classroom behavior and was more efficient than teacher-

mediated intervention.

Raines and Ellis (1982) reported that a computer-assisted intervention to facilitate behaviorchange in such areas as smoking, weight, exercise, and drinking was helpful for 92 % of all

users. A program delivered on a large scope, the Staywell Lifestyle Change Program, has

applied cognitive-behavioral methods to similarly effect health changes (Lau & Hall, 1983).

Another computer-based health education program, the Body Awareness Resource Network

(BARN) Project, has achieved a measure of success by giving the computer a

personality (Hawkins, Bosworth, Chewning, Day, & Gustofson, 1985).

Moncher et al. Page 2

Comput Human Behav. Author manuscript; available in PMC 2007 March 26.

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The BARN projecta computer-based health promotion system for teenagers and their

familieswarrants additional discussion. Longitudinal field testing following 800 subjects

drawn from an original sample of 2400 adolescents provides strong evidence of the efficacy

of computer-based health promotion (CBHP) (Gustafson, Bosworth, Chewning, & Hawkins,

1986). An extensive review of available health-related software indicated a paucity of material

that was either interactive, integrative, or goal oriented in approach. BARN was developed to

address this need. In the testing phase, a number of positive results were found. Significantly,

the authors noted that:

BARN reaches adolescents who, in the pretest, generally reported more risk-taking behavior

(e.g. more smoking, higher rate of sexual intercourse, more serious consequences from alcohol

or other drug use, etc.) than nonusers. Thus the computer may be a particularly powerful means

of reaching people who are making bad health decisions (Gustafson et al., 1986, p. 9).

Utilizing technological advances in health-related software design, BARN is on the leading

edge of computer-based health promotion systems for young people. Its early successes

indicate both the feasibility of such systems, and the potential efficacy they will achieve.

The areas of learning theory, decision science, artificial intelligence, simulation, change theory,

and group processes have developed models that can provide the structure of a CBHP

(computer based health promotion) system. The knowledge base of health promotion has

increased to the point where impressive stores of data, literature, and expertise have been

developed.

What must be done now is to carefully design high quality CBHP systems to help people

solve their health-related behavior problems by compensating for documented human

weaknesses in complex problem solving. These systems can then interface with group and

individual problem solving (Gustafson et al., 1986, p. 34).

While CBHP may represent immeasurable value for health education in general, comparative

questions concerning delivery methodology are often raised. In this regard, Deardorff (1986)

compared the relative efficacy of curricula delivered via computer, face-to-face, and written

materials. Outcome data revealed a positive assessment of computerized and face-to-face

formats, with the written format assessed less positively. Deardorff reported that subjects spent

more time interacting with the computer than during either written or face-to-face formats.

Furthermore, the study discovered a relationship between subjects time spent interacting with

computer and their free recall of information presented (r= .39,p < .01).

Additionally, Wise and Wise (1987), in a comparison study of computer-administered and

paper-administered achievement tests with elementary school children found that, while no

significant mean test score differences were noted, computer feedback stimulated state-anxiety

among high mathematics achievers leading to the conclusion that additional research is needed

regarding feedback as a mechanism in computer-aided instructional materials.

Another study found that a computer-aided behavioral smoking cessation program was at least

as effective in promoting abstinence as were traditional face-to-face methods. Perhaps of

greater long-term import was the finding that all participants in this program indicated that

they would not have attempted to quit smoking at this time, had there not been this

program, (Schneider, 1986, p. 284).

In addition to comparative educational techniques, a second lingering question for CAI and

CBHP in particular has been personal or receiver variables of research subjects. Lewis and

Cooney (1987) used computer-aided instruction to assess effects of differential educational

styles on mathematics achievement. Locus of control and field dependence/independence



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variable measures were examined subsequent to computer-based interventions employing

competitive and individualistic feedback mechanisms against a control group receiving

instruction with no feedback other than that normally provided by the system. While no

significant main effects were observed, it was noted that treatment conditions differentially

effected performance by gender, with males progressing at a higher rate under the competitive

condition and females progressing at a higher rate under the individualistic feedback condition

than was observed under the competitive condition. Study results also noted that while

performance was affected by condition, differences in academic locus of control were obviatedby this study.

Other recent studies reported in these pages have shown that, among certain groups, computer-

based instrument administrations are at least as valid and reliable as are paper-pencil

administrations of equivalent instruments (Harrell, Honaker, Hetu, & Oberwager, 1987;

Lambert, Reagen, Rylee, & Skinner, 1987), and research is being done to isolate attitudinal

variables that may be used to enhance effective software development for both instructional

and testing purposes among various subject groups (Burke, Normand, & Raju, 1987; Nickell

& Pinto, 1987; Rozensky, Feldman-Honor, Rasinski, Tovian, & Herz, 1986).

Of particular interest, given the minority focus of our research, is a recent, well controlled study

by Pulos and Fisher (1987) measuring adolescents interest in computers by attitude and

socioeconomic status. School A was located in a large, urban setting. Ethnic composition was51% black, 35% hispanic, 4% asian and 8% white. Furthermore, 41% of the families received

AFDC. Computer exposure was minimal. School B was located in a suburban area, was

predominantly white, and reported only 5 % of the families receiving AFDC. Further, all

adolescents in school B had significant computer exposure.

The instruments administered to all students were designed to measure interests in computer

and other activities. In addition, open-ended questions were included to elicit student views

about the characteristics of adolescents who like computers. These data were subjected to a

principal components analysis in which four principal components were found including

typical adolescent activities, adult-approving activities, intellectual activities, and physical

activities. Component scores were calculated for each, and compared across the two school

groups.

The results indicated that, while in general most adolescents were indifferent to computers,

there was a school difference in computer interest with students from the lower SES school

showing significantly more interest than those in the middle-class school. Pulos and Fisher

suggest that this difference might be due largely to the lack of computer exposure of the lower

SES group. If this is so, might we not capitalize on this deficit in the service of prevention?

They further suggest that computer interest was subjectively associated with more intellectual,

adult approved behaviors. Perhaps this association is the basis for the low interest in computers

found in this study, since many adolescents do not want to be seen as intelligent and will tend

to avoid activities that may lead them to be seen as intellectual by their peers. (Pulos & Fisher,

1987, p. 35).

It appears clear that persons in general do tend to respond positively to computer administered

instruction. For microcomputer-based interventions to help black adolescents avoid drug andalcohol abuse, research is needed to ascertain specific attitudes of black adolescents toward

computers. Given both cultural and socioeconomic differentials, it must be determined both

whether this group in particular will respond to cognitive-behavioral, health related messages

delivered via computer. Furthermore, responsive hardware and software configurations must

be developed to tap the specific needs of black youth, thus promoting maximum response. This



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study was designed to increase empirical knowledge on computer-aided instruction for drug

abuse prevention among youth from disadvantaged, ethnic-racial minority backgrounds.

METHOD

Subjects

Study subjects were 26, 11 through 14-year-old black females and males from economically

disadvantaged homes located in three boroughs of New York City. Recruited through NewYork City Board of Education Community School District schools and from the New York

Housing Authority, subjects gave their informed consent and obtained parental consent prior

to study participation. Subjects understood the nature of the study and were able to withdraw

from it at any time without penalty. No enticements for study involvement were offered subjects

or their parents. Table 1 presents demographic characteristics of study subjects.

Procedure

The study was designed to empirically assess the potential of microcomputer-based

intervention with black adolescents from economically disadvantaged backgrounds.

Accordingly, study subjects were given a sample task via a microcomputer system, followed

by a post-intervention measurement battery. Post-intervention questionnaires contained Likert

scaled and open-ended items. Together, questionnaire items measured contextual andinteractional variables appropriate to the microcomputer task and software. Contextual

variables measured in the battery included the amount of material retained, or learned, by

subjects upon completion of computer interaction. These variables covered subjects feelings

about using computers respective to such factors as intimidation, mastery, enjoyment, and

involvement.

Interactional variables measured in the questionnaire battery included the degree to which

subjects would interact with the microcomputer over time. This degree of interaction was

estimated by time-interval measures of subjects eyes on microcomputer screen and their

fingers on the entry pad keys. Additional measures of interactional parameters were obtained

by assessments of subjects ability to follow software and instructor comments and subjects

attention span in time on the computers. Other items measured subjects perceptions of the

microcomputer as a receptive learning and teaching medium, subjects prior computerexperience, and their current accessibility to and use of microcomputers.

Computer Task

Five subjects were concurrently tested, each individually operating a computer for

approximately 15 minutes. Each student was assigned to a computer, and urged to observe the

demonstration program running on the screen prior to attempting to use the software. In

addition to the demonstration, a brief description of the learning task and basic keyboard use

was presented. The description was made uniform across subjects to avoid potential confound

resulting from differential explanations.

As the study primarily focused on user interest in using computers, the software selection

criteria centered on reduction of as many barriers to use as possible. First, given the limited

duration of the interaction between student and software, the teaching technique and computerinterface had to provide easy access to the material with a minimum of external guidance. In

such a case, interface-generated frustrations of inexperienced users could be kept to a bare

minimum. Second, the content had to represent a known knowledge category students were

used to dealing with (rather than health-related content less frequently encountered).



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Against these criteria, we chose an educational package that allowed subjects to continually

interact with the software in response to computer-generated questions and tasks. The software

itself was a well-known geography package. The primary task was to place countries in an area

map in response to the countrys name or other information about that country. The human-

computer interface employed a standard ARROW - key/ENTER control technique for moving

the cursor and making selections. The primary visual nature of the software thus presented

students with meager reading requirements. Additionally, the various user-selectable methods

of questioning available provided interest and challenge as well as individualized levels ofdifficulty.

Measurement

Measurements of subjects were taken at two periods. During the computer task, two observers

recorded data on each subjects overt interactions with the software and microcomputer.

Immediately after the computer task, subjects were individually interviewed about their

experience with the microcomputer and software.

Observations

The observational protocol required two observers, each of whom made a consecutive, minute

by minute sweep of each subject (generally in cohorts of five), over a fifteen minute period.

Each observer was provided with a coding sheet with the following instructions: In the top halfof each box, record a (1) if the child is looking at the screen. Record a (0) if the child is not

looking at the screen. In the bottom half of each box, record a (1) if the childs fingers are

depressing keys. Record a (0) if the childs fingers are not depressing keys. Use of two raters

provided a measure of inter-rater reliability. In addition, a facilitator was present to give the

children initial instructions, and answer any questions asked during the intervention period.

The observers did not interact with subjects.

Posttest Interview

Following subjects completion of the intervention, they were taken into an interviewing room

and administered a posttest questionnaire designed to measure values on the variables described

above. Each child was assured by the interviewer that the information provided was

confidential and to be used only for purposes of analysis. Each subject, at the end of the

interview, was encouraged to provide input regarding the computer in general, and,

specifically, what could be done to make working with the machine more interesting, enjoyable,

and valuable as a learning experience.

RESULTS

Observational Data

Inter-rater reliability was 88.5 % as measured across the three variables of time (15 min), eye

interaction with the screen and finger interactions with the keyboard. Cell size precluded any

statistical measures of significance, as no chi-square related tests could be performed.

Interaction effects were observed as follows. First, the ration of overall time spent interacting

as measured on either of the two interacting variables was 85.7 %. Second, subjects had both

eyes and hands on the computer 85.1% of the time across the observed measurement period.Again, cell size precludes analysis of significance.

Third, the ratio of eyes off the screen was 0%. Finally, the ratio of noninteraction during the

fifteen minutes intervention was 1.3%.



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Responses

When asked about positive reactions to computer-generated tasks, 23.1% of the participants

answered with the general response, I liked everything. About one-third of the subjects (34.6

%) were more specific, indicating that they liked using the computer. Nearly one-fourth of

the subjects (23.1%) mentioned a software-specific variable. An additional 11.5% of the

subjects mentioned using the keyboard as an enjoyable experience (Table 2).

Subjects negative reactions to computer tasks mirrored the positive responses. About 42% ofthe subjects found no disagreeable task aspects. Additionally, 19.2% of the participants

objected only to having to stop. The more general question measuring overall interaction

disagreement confirmed these results, with 57.7 % of the subjects enjoying all interaction

aspects. Nearly 27 % of the adolescents singled out a bothersome software-specific variable.

About 12 % of the subjects mentioned a lack of screen clarity due to the size of the display.

Only three participants (11.5 %) mentioned problems with the level of difficulty, finding it

either too difficult or too simplistic. All participants who mentioned the difficulty level had

some previous computer experience (Table 3).

Tables 4 and 5 depict subjects responses to questions regarding possible hardware, software,

or process modifications that would have increased subject enjoyment.

As with all communications, computer-delivered education materials are subject to variationsin quality specific to those targeted for receipt of intended information. Values for specific

receiver variables were obtained during posttest interviews and subsequently correlated with

general reactions to the software test session. Receiver variables included gender, age, previous

computer experience, familiarity with video games, and overall computer availability.

Regarding previous computer experience, participants fell into three overall categories; those

with no experience, those with less than one year of experience, and those with more than one

year of experience. Seventy-seven point eight percent or 14 of 18 boys tested, had some

previous experience (53.8% of the total sample). Over 63 %, or 5 of the 8 girls tested, had

previous experience (19.2 % of the total sample), indicating differential gender effects (Table

6).

In another analysis, 77.8% of the boys tested expressed enjoyment of video games, with 72.3%of them actively participating in play. By contrast, 87.5% of the girls tested expressed similar

enjoyment with only 25% actively involved in their use (Table 7 and 8).

When asked questions concerning the helpfulness and necessity of instructions given by the

software and the interventionist, 34.6% of all participants felt that the combination of computer/

intervention instructions was adequate for overall task performance, including hardware use.

Except for general responses such as the instructions helped me learn how to use the

computer, the most dominant response category related to questions on aspects of keyboard

use. Nearly 43 % of all subjects made mention of the keyboard, stating either that the

instructions regarding its use were helpful or were needed.

Participants were asked three different questions designed to determine their relative preference

for computer vs. human materials delivery. Results indicate a disparity based upon the type ofinformation to be conveyed. In response to a general question concerning preference for

computer or teacher-delivered classroom work, computer delivery was the clear choice.

(69.2%).

When asked to choose between computer or human delivery in general, the participants split

evenly (Table 9).



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In direct contrast, when asked to choose between computer or human delivery of information

or counseling regarding drug or alcohol abuse, there was an 8 to 1 split in favor of human

delivery, with 18 respondents (69.2 %) choosing such interventions and only 2 participants

(7.7 %) preferring computer delivery. Almost 20 % of the subject had no specific preference.

For all three questions, the primary reason for choosing human delivery related to the inanimate

nature of a computer and its inability to understand or otherwise relate to human problems.

This notion was best articulated by one subject who stated, Computer cant take drugs. (Table

10).

When asked about other general factors involved in deciding upon computer or human content

delivery, participants generally felt that a computer was more fun than a person. There was

no agreement on whether general (nondrug or alcohol specific) information could be gained

more effectively from either source. Other factors mentioned favoring either computer or

human delivery preferences included I dont have to write on a computer,, I like

computers,, and A computer cant yell at me (Table 11).

Responses indicated a general trend towards computer use in school. Though 27.8 % of the

boys (five subjects) had home access to a computer, none of the girls had such access (Table

12).

Regarding types of activities most often performed on a computer, the largest response (42.9%)

indicated game playing, with learning activities second (23.8%; Table 13).

DISCUSSION

In this study, black adolescents attitudes toward educational content is general, and toward

drug and alcohol information delivery in particular, appeared to be a significant intervening

variable that could alter the overall efficacy of computer-delivered interventions. With

reference to McGuires persuasion matrix, software designs must take into account the possible

disbelief and/or suspicion of targeted subjects in the ability of the computer to exhibit the

flexibility, empathy, and sensitivity requisite for understanding human problems.

Consequently, research must ascertain black adolescents specific attitudes towards computers.

Given both cultural and socio-economic differentials, it must be determined whether this group

will respond positively to computer-delivered interventions and, if it will, how best can

hardware and software be designed to promote maximum response.

Further, black adolescents must have access to computer hardware. Largely due to economic

maldistributions, such access is more the exception than the rule. Still, cost-cutting trends,

aggressive marketing, and in-kind contributions of equipment bode well for rising numbers of

microcomputers in the homes and schools of black american youth (Becker, 1984; Ingersoll

& Smith, 1984). Also, as more ethnic-specific software is developed and tested, schools and

organizations serving predominantly black populations may be more likely to make the

requisite investments to achieve the long term economies of scale such programs should

provide.

Software must also be developed to provided multi-screened, comprehensive, and easily

accessed information on all facets of drug and alcohol of specific interest to this cohort, as well

as to deliver differentially paced, effective and age appropriate prevention interventions.

Results from the present study indicate that these aspects of modular design are critical if the

general trend noted towards computer enjoyment and use is to be extended to dissemination

of drug and alcohol information and preventive interventions. Steps must be taken within the

software design to instill a sense of confidence in the user through repetitive demonstration of



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human-like responsiveness, early in the intervention experience. This is highlighted by the

reluctance of the study cohorts toward computer interactions regarding substance use,

inconsistent with the overall positive attitude exhibited by the subjects about working with

computers in general.

The economic imperatives of software development dictate that production must meet market

demand. Consequently, most instructional software is geared currently toward majority culture,

middle- and upper-income americans, seldom tapping the life experience and everyday realitiesof ethnic-racial minority, lower income, and disadvantaged populations. To successfully

develop such interactive and effective software, focus groups must be implemented in order

to gather further information from the cohort regarding preferences, as well as to test efficacy

of software during the development process.

Areas for further research include large scale, controlled testing of computer-based vs. human

skills-interventions using such newly developed software. Such studies might be longitudinal

in nature and include booster sessions utilizing both changes in attitudinal factors, knowledge

retention, and drug/alcohol use as the cohort reaches high school age.

Finally, studies combining both human and computer-aided content delivery would provide

data regarding possible synergistic or suppressor interactions. Possibly, positive correlations

could have been discovered between various interview parameters and psychometric measures

obtained during the test session. However, logistics of the test experience did not permit subject

identification cross-linking of data. More sophisticated measurements development,

significantly larger samples, and appropriate coding measures provide additional areas for

further study.

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Table 1

Subject Demographic Characteristics

AGE GRADE

N % Mean Min Max Mean Min Max

Boys 18 69.2 13.83 11 16 7.22 5 9Girls 8 30.8 14.75 12 16 8.25 7 10

Total 26 100.0% 14.12 11 16 7.54 5 10


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Table 2

Subjects Positive Reactions to Computer Task

Items Subjects Liked Best Items Subjects Enjoyed Most

Item Frequency Percent Item Frequency Percent

Software 6 23.1 Software 18 69.3Using computer 9 34.6 Directions 3 11.5

All tasks 6 23.1 Experience 1 3.8Keyboard 3 11.5 Other 2 7.7


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Table 3

Subjects Negative Reactions to Computer Task

Items Subjects Liked Least Items Subjects Disliked Most

Item Frequency Percent Item Frequency Percent

No response 2 7.7 Software 7 26.9Having to stop 5 19.2 Difficulty level 3 11.5

Visual limitation 3 11.5 Nothing 15 57.7Nothing 11 42.2 Other 1 3.8Keyboard 1 3.8


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Table 4

Elements to Increase Subjects Enjoyment of the Task

Elements Mentioned

Customization 11.5% (3)Different content 23.1% (6)Alternate technique 23.1% (6)Other 11.5% (3)

Nothing 19.2% (5)No response 11.5% (3)


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Table 5

Suggested Modifications to Increase Enjoyment

Item to Modify Frequency Percent

Nothing 9 34.6Use games 6 23.1More access 3 11.5*Use joystick 3 11.5*

Content 7 26.9*Speech 2 7.6*Customization 2 7.7Software mechanics 1 3.8Sound (other than speech) 1 3.8

Note. Response categories for this question are not mutually exclusive. Two responses were recorded for each respondent when applicable. As such,

percentages reflect how many respondents mentioned any single item. Asterisk items are those mentioned as the second response in addition to other

items. All other responses represent the first response.


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Table 6

Subjects Prior Computer Experience

Prior Experience

N % Less than 1 year 2 or more years

Boys 14 77.8% 33.3% (6) 44.4% (8)Girls 5 62.5% 0.0 62.5% (5)


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Table 7

Subjects Preference and Use of Video Game

Enjoyment of video games

Yes No

Boys 77.8% (14) 22.2 (4)Girls 87.5% (7) 12.5 (1)

Sample 80.8% (21) 19.2 (5)


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Table 8

Subjects Average Time Spent Playing Video Games, by Gender

Time Spent Using Video Games Each Week

None < 15 min 1530 min 12 hrs 2 + hrs

Boys 27.8% (5) 11.1 % (2) 16.7% (3) 5.6% (1) 38.9% (7)Girls 75.0% (6) 0.0% 0.0% 12.5% (1) 12.5% (1)

Total 42.3% (11) 7.7% (2) 11.5% (3) 7.7% (2) 30.8% (8)


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Table 9

Subjects Preferences for Computer vs. Person Instruction

Preference Computer Person Either

Computer/Person 42.3% (11) 42.3% (11) 7.7% (2)Computer/Classroom 65.4% (17) 26.9% (7) 7.7% (2)Computer/Counselor 7.7% (2) 69.2% (18) 19.2% (5)


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Table 10

Factors Cited as Important in Choosing Human Delivery for Drug and Alcohol Abuse Prevention Content

Factor Cited Frequency Percent

More knowledge (quantity) 9 42.3Better explanation/response 2 15.4*

Need to explain self 1 3.8Lack of personalism 8 30.8*

Feelings 1 3.8Speaking ability/limitation 1 3.8Self-pacing of learning 1 3.8Other 4 15.1*

Note. Response categories are not mutually exclusive. Two responses were recorded for each respondent when applicable. As such, percentages reflect

how many respondents mentioned any single item. Asterisk items are those mentioned as the second response in addition to other items. All other responses

represent the first response.


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Table 11

Factors Cited as Important in Choosing General Computer or Human Content Delivery

Frequency Percent

More fun 5 19.2Help 2 7.7More information 4 15.4Understanding 1 3.8

Interpersonal interaction 2 7.7No reason given 2 7.7


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Table 12

Computer Availability

Computer Availability in Percentages and (Frequency)

Home School

Boys 27.8% (5) 83.6% (15)Girls 0.0% 75.0% (6)

Sample 19.2% (5) 80.8% (21)


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Table 13

Activities on Computer

Activities Mentioned (in Percentages)

Item Boys Girls Sample

Games 43.8 40.0 42.9Learning 18.8 40.0 23.8

Word processing 12.5 0.0 9.5Programming 12.5 0.0 9.5Other 0.0 20.0 4.8

Nothing 12.5 0.0 9.5


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